UCLA

A tenet of precision medicine is the ability to predict a clinical response or outcome for a given individual. The creation of predictive disease models as a part of this evidence-based process has increased in recent years as electronic medical data and machine learning techniques have grown in popularity. A number of methods are available for testing internal validity when developing models. However, the performance of developed disease models must also be tested in external populations. This practice of external validation, or transportability, can help determine the extent to which information in a predictive model can be applied generally across population samples. Model performance can suffer when applied in external settings because populations have inherent differences or data was collected with different practices. A number of impediments, particularly in reporting, continue to hinder the widespread application of transportability methods. This dissertation addresses these concerns using a methodology for improving the classification of models and evaluating a proposed technique for partially adjusting problematic models. A set of simulations are proposed, taking advantage of a minimal set of published values to extend reported findings through bootstrap analysis. Interpretations derived from this method are able to guide the assignment and selection of models by transport levels. Causal transportability analysis, a previously proposed transportability theory in graphical models, is also evaluated for use in partial adjustment scenarios. The resulting process allows for evaluation of model transportability with minimal information and for assessing model adjustments. These investigations serve as useful tools for future transportability analysis. In addition, the results of this work introduce new items that can be added to reporting guidelines and support current trends to establish improved validation frameworks.